{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial 3\n",
    "\n",
    "\n",
    "10/03/2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import *\n",
    "from sklearn.datasets import load_boston\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCICE 1 -  Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = [[1,-2],[3,0]]\n",
    "B = [[4,4],[-1,1]]\n",
    "C = [[7,0]]\n",
    "D = [[-3,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.matrix(A)\n",
    "B = np.matrix(B)\n",
    "C = np.matrix(C)\n",
    "D = np.matrix(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 6,  2],\n",
       "        [12, 12]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A * B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[16, -8],\n",
       "        [ 2,  2]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B * A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[32,  0],\n",
       "        [ 0,  2]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B * np.transpose(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[17, 15],\n",
       "        [15, 17]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(B) * B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[  7, -14]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C * A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-8],\n",
       "        [ 4]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B *np.transpose(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 0.        ,  0.33333333],\n",
       "        [-0.5       ,  0.16666667]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy.linalg import inv\n",
    "inv(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCICE 2 - Multiple Linear Regression \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gestation = np.array([35,40,42,37])\n",
    "smoke = np.array([1,0,1,0])\n",
    "weight = np.array([2440,3226,3301,2729])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gestation</th>\n",
       "      <th>smoke</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>2440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>3226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>3301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gestation  smoke  Weight\n",
       "0         35      1    2440\n",
       "1         40      0    3226\n",
       "2         42      1    3301\n",
       "3         37      0    2729"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'gestation':gestation , 'smoke': smoke,  'Weight': weight}\n",
    "df = pd.DataFrame(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Hand "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.matrix([[35,1],[40,0],[42,1],[37,0]])\n",
    "y=  np.matrix([[2440, 3226, 3301, 2729]])\n",
    "step1 = np.transpose(X) * X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "leftpart = inv(step1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 3.34057124e-04, -1.28611993e-02],\n",
       "        [-1.28611993e-02,  9.95156172e-01]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leftpart "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.84416225652001"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.000334057124*454055 +  -0.0128611993* 5741"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-126.5002647095007"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-0.0128611993 * 454055 + 0.995156172*5741"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rightpart = np.transpose(X)* np.transpose(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[454055],\n",
       "        [  5741]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rightpart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[  77.84416235],\n",
       "        [-126.50025054]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leftpart * rightpart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = 77.84*gestation-126.50*smoke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "ybar = y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([2440, 3226, 3301, 2729])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "505614.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## calculate the sum of squared deviations\n",
    "St = sum((y-ybar)**2)\n",
    "St"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85424.90479999986"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## calculate the sum of squares of the errors\n",
    "Sr = sum((y-yhat)**2)\n",
    "Sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8310471925223593"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##coefficient of determination\n",
    "R2 = 1-float(Sr/St)\n",
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Weight'], axis = 1)\n",
    "y = df['Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=False, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression(fit_intercept = False)\n",
    "reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  77.84416235, -126.50025054])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for training set\n",
      "--------------------------------------\n",
      "R2 score is 0.83\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "r2 = round(reg.score(X, y),2)\n",
    "print(\"The model performance for training set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.scatterplot(x=\"gestation\", y=\"Weight\", hue=\"smoke\", data=df);\n",
    "plt.setp(g.get_legend().get_texts(), fontsize='22');\n",
    "g.set_xlabel(\"Gestation\",fontsize=20)\n",
    "g.set_ylabel(\"Weight\",fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wgt</th>\n",
       "      <th>Gest</th>\n",
       "      <th>Smoke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2940</td>\n",
       "      <td>38</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3130</td>\n",
       "      <td>38</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2420</td>\n",
       "      <td>36</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2450</td>\n",
       "      <td>34</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2760</td>\n",
       "      <td>39</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Wgt  Gest Smoke\n",
       "0  2940    38   yes\n",
       "1  3130    38    no\n",
       "2  2420    36   yes\n",
       "3  2450    34    no\n",
       "4  2760    39   yes"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('birth-smoke.txt', sep='\\t')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Response (y): birth weight (Weight) in grams of baby\n",
    "- Potential predictor (x1): Smoking status of mother (yes or no)\n",
    "- Potential predictor (x2): length of gestation (Gest) in weeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.setp(g.get_legend().get_texts(), fontsize='22');\n",
    "g=sns.scatterplot(x=\"Gest\", y=\"Wgt\", hue=\"Smoke\", data=data);\n",
    "g.set_xlabel(\"Gestation\",fontsize=20)\n",
    "g.set_ylabel(\"Weight\",fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode the binary variable Smoke in 1 and 0 \n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(data.Smoke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['no', 'yes']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.transform(data.Smoke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Smoke_encode'] = le.transform(data.Smoke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wgt</th>\n",
       "      <th>Gest</th>\n",
       "      <th>Smoke</th>\n",
       "      <th>Smoke_encode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2940</td>\n",
       "      <td>38</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3130</td>\n",
       "      <td>38</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2420</td>\n",
       "      <td>36</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2450</td>\n",
       "      <td>34</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2760</td>\n",
       "      <td>39</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2440</td>\n",
       "      <td>35</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3226</td>\n",
       "      <td>40</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3301</td>\n",
       "      <td>42</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2729</td>\n",
       "      <td>37</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3410</td>\n",
       "      <td>40</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2715</td>\n",
       "      <td>36</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3095</td>\n",
       "      <td>39</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3130</td>\n",
       "      <td>39</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3244</td>\n",
       "      <td>39</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2520</td>\n",
       "      <td>35</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2928</td>\n",
       "      <td>39</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3523</td>\n",
       "      <td>41</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3446</td>\n",
       "      <td>42</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2920</td>\n",
       "      <td>38</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2957</td>\n",
       "      <td>39</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3530</td>\n",
       "      <td>42</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2580</td>\n",
       "      <td>38</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3040</td>\n",
       "      <td>37</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3500</td>\n",
       "      <td>42</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3200</td>\n",
       "      <td>41</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3322</td>\n",
       "      <td>39</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3459</td>\n",
       "      <td>40</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3346</td>\n",
       "      <td>42</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2619</td>\n",
       "      <td>35</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3175</td>\n",
       "      <td>41</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2740</td>\n",
       "      <td>38</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2841</td>\n",
       "      <td>36</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Wgt  Gest Smoke  Smoke_encode\n",
       "0   2940    38   yes             1\n",
       "1   3130    38    no             0\n",
       "2   2420    36   yes             1\n",
       "3   2450    34    no             0\n",
       "4   2760    39   yes             1\n",
       "5   2440    35   yes             1\n",
       "6   3226    40    no             0\n",
       "7   3301    42   yes             1\n",
       "8   2729    37    no             0\n",
       "9   3410    40    no             0\n",
       "10  2715    36   yes             1\n",
       "11  3095    39    no             0\n",
       "12  3130    39   yes             1\n",
       "13  3244    39    no             0\n",
       "14  2520    35    no             0\n",
       "15  2928    39   yes             1\n",
       "16  3523    41    no             0\n",
       "17  3446    42   yes             1\n",
       "18  2920    38    no             0\n",
       "19  2957    39   yes             1\n",
       "20  3530    42    no             0\n",
       "21  2580    38   yes             1\n",
       "22  3040    37    no             0\n",
       "23  3500    42   yes             1\n",
       "24  3200    41   yes             1\n",
       "25  3322    39    no             0\n",
       "26  3459    40    no             0\n",
       "27  3346    42   yes             1\n",
       "28  2619    35    no             0\n",
       "29  3175    41   yes             1\n",
       "30  2740    38   yes             1\n",
       "31  2841    36    no             0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['Wgt','Smoke'], axis = 1)\n",
    "y = data['Wgt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2803.69342567, 3048.23746586, 2517.49287944, 2475.83637339,\n",
       "       2946.79369879, 2374.39260632, 3334.4380121 , 3376.09451814,\n",
       "       2905.13719274, 3334.4380121 , 2517.49287944, 3191.33773898,\n",
       "       2946.79369879, 3191.33773898, 2618.93664651, 2946.79369879,\n",
       "       3477.53828521, 3376.09451814, 3048.23746586, 2946.79369879,\n",
       "       3620.63855833, 2803.69342567, 2905.13719274, 3376.09451814,\n",
       "       3232.99424503, 3191.33773898, 3334.4380121 , 3376.09451814,\n",
       "       2618.93664651, 3232.99424503, 2803.69342567, 2762.03691963])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = reg.predict(X)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance is\n",
      "--------------------------------------\n",
      "RMSE is 109.98150794324624\n",
      "R2 score is 0.9\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rmse = (np.sqrt(mean_squared_error(y, y_predict)))\n",
    "r2 = round(reg.score(X, y),2)\n",
    "\n",
    "print(\"The model performance is\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Study - Boston House Pricing\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source :\n",
    "https://medium.com/@amitg0161/sklearn-linear-regression-tutorial-with-boston-house-dataset-cde74afd460a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
      "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
      "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
      "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
      "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
      "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
      "\n",
      "   PTRATIO       B  LSTAT  PRICE  \n",
      "0     15.3  396.90   4.98   24.0  \n",
      "1     17.8  396.90   9.14   21.6  \n",
      "2     17.8  392.83   4.03   34.7  \n",
      "3     18.7  394.63   2.94   33.4  \n",
      "4     18.7  396.90   5.33   36.2  \n"
     ]
    }
   ],
   "source": [
    "bos = pd.DataFrame(boston.data)\n",
    "bos.columns = boston.feature_names\n",
    "bos['PRICE'] = boston.target\n",
    "print(bos.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- CRIM per capita crime rate by town\n",
    "\n",
    "- ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n",
    "\n",
    "- INDUS proportion of non-retail business acres per town\n",
    "\n",
    "- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
    "\n",
    "- NOX nitric oxides concentration (parts per 10 million)\n",
    "\n",
    "- RM average number of rooms per dwelling\n",
    "\n",
    "- AGE proportion of owner-occupied units built prior to 1940\n",
    "\n",
    "- DIS weighted distances to five Boston employment centres\n",
    "\n",
    "- RAD index of accessibility to radial highways\n",
    "\n",
    "- TAX full-value property-tax rate per $10,000\n",
    "\n",
    "- PTRATIO pupil-teacher ratio by town\n",
    "\n",
    "- B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
    "\n",
    "- LSTAT % lower status of the population\n",
    "\n",
    "- MEDV Median value of owner-occupied homes in $1000’s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
      "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
      "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
      "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
      "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
      "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
      "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
      "\n",
      "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
      "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
      "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
      "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
      "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
      "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
      "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
      "\n",
      "            LSTAT       PRICE  \n",
      "count  506.000000  506.000000  \n",
      "mean    12.653063   22.532806  \n",
      "std      7.141062    9.197104  \n",
      "min      1.730000    5.000000  \n",
      "25%      6.950000   17.025000  \n",
      "50%     11.360000   21.200000  \n",
      "75%     16.955000   25.000000  \n",
      "max     37.970000   50.000000  \n"
     ]
    }
   ],
   "source": [
    "print(bos.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(bos.PRICE);\n",
    "plt.title(\"Boxplot of the price\")\n",
    "plt.xlabel('')\n",
    "plt.ylabel('');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "corr = bos.corr()\n",
    "sns.heatmap(corr, annot=True);\n",
    "plt.title(\"Correlation Matrix\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "features = ['LSTAT', 'RM', 'CRIM' ]\n",
    "target = bos['PRICE']\n",
    "\n",
    "for i, col in enumerate(features):\n",
    "    plt.subplot(1, len(features) , i+1)\n",
    "    x = bos[col]\n",
    "    y = target\n",
    "    plt.scatter(x, y, marker='o')\n",
    "    plt.title(\"Variation in House prices\")\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('\"House prices in $1000\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regression Model with one variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_rooms = bos.RM\n",
    "y_price = bos['PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 1)\n",
      "(506, 1)\n"
     ]
    }
   ],
   "source": [
    "X_rooms = np.array(X_rooms).reshape(-1,1)\n",
    "y_price = np.array(y_price).reshape(-1,1)\n",
    "\n",
    "print(X_rooms.shape)\n",
    "print(y_price.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404, 1)\n",
      "(102, 1)\n",
      "(404, 1)\n",
      "(102, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(X_rooms, y_price, test_size = 0.2, random_state=5)\n",
    "\n",
    "print(X_train_1.shape)\n",
    "print(X_test_1.shape)\n",
    "print(Y_train_1.shape)\n",
    "print(Y_test_1.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for training set\n",
      "--------------------------------------\n",
      "RMSE is 6.972277149440585\n",
      "R2 score is 0.43\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reg_1 = LinearRegression()\n",
    "reg_1.fit(X_train_1, Y_train_1)\n",
    "\n",
    "y_train_predict_1 = reg_1.predict(X_train_1)\n",
    "rmse = (np.sqrt(mean_squared_error(Y_train_1, y_train_predict_1)))\n",
    "r2 = round(reg_1.score(X_train_1, Y_train_1),2)\n",
    "\n",
    "print(\"The model performance for training set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot the model fitted with a line\n",
    "prediction_space = np.linspace(min(X_rooms), max(X_rooms)).reshape(-1,1) \n",
    "plt.scatter(X_rooms,y_price)\n",
    "plt.plot(prediction_space, reg_1.predict(prediction_space), color = 'black', linewidth = 3)\n",
    "plt.ylabel('value of house/1000($)')\n",
    "plt.xlabel('number of rooms')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Linear regression Model with all the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = bos.drop('PRICE', axis = 1)\n",
    "y = bos['PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for training set\n",
      "--------------------------------------\n",
      "RMSE is 4.6520331848801675\n",
      "R2 score is 0.75\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=42)\n",
    "\n",
    "reg_all = LinearRegression()\n",
    "reg_all.fit(X_train, y_train)\n",
    "\n",
    "# model evaluation for training set\n",
    "\n",
    "y_train_predict = reg_all.predict(X_train)\n",
    "rmse = (np.sqrt(mean_squared_error(y_train, y_train_predict)))\n",
    "r2 = round(reg_all.score(X_train, y_train),2)\n",
    "\n",
    "print(\"The model performance for training set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.13055924e-01,  3.01104641e-02,  4.03807204e-02,  2.78443820e+00,\n",
       "       -1.72026334e+01,  4.43883520e+00, -6.29636221e-03, -1.44786537e+00,\n",
       "        2.62429736e-01, -1.06467863e-02, -9.15456240e-01,  1.23513347e-02,\n",
       "       -5.08571424e-01])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_all.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.24675099392408"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_all.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
